#import libraries
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

#load the data
df = pd.read_csv('./daten/WHO_adolescent_pop_1950_2050_age_10_19_both_sexes.csv')
df

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23937 entries, 0 to 23936
Data columns (total 7 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Year                                  23937 non-null  int64  
 1   Country_ISO_3_code                    23937 non-null  object 
 2   Country                               23937 non-null  object 
 3   WHO_region                            23533 non-null  object 
 4   World_bank_income_group               21816 non-null  object 
 5   Adolescent_population_in_millions     23937 non-null  float64
 6   change_from_previous_year_in_percent  23937 non-null  object 
dtypes: float64(1), int64(1), object(5)
memory usage: 1.3+ MB

from summarytools import dfSummary
dfSummary(df)

unique_who_regions = df['WHO_region'].unique()
unique_income_groups = df['World_bank_income_group'].unique()

count_who_regions = df['WHO_region'].value_counts()
count_income_groups = df['World_bank_income_group'].value_counts()

print(unique_who_regions)
print(unique_income_groups)
print(count_who_regions)
print(count_income_groups)

['Eastern Mediterranean' 'Europe' 'Africa' 'Western Pacific' 'Americas'
 'South-East Asia' nan]
['Low income' 'Upper middle income' 'High income' 'Lower middle income'
 nan]
WHO_region
Europe                   6161
Americas                 5454
Africa                   5151
Western Pacific          3434
Eastern Mediterranean    2222
South-East Asia          1111
Name: count, dtype: int64
World_bank_income_group
High income            8585
Upper middle income    5454
Lower middle income    5151
Low income             2626
Name: count, dtype: int64

# detect missing values for 2020
df[(df['Year'] == 2020) & 
    ((df['WHO_region'].isna()) | (df['World_bank_income_group'].isna()))]

df[(df['Year'] == 2020) & 
    ((df['WHO_region'].isna()) | (df['World_bank_income_group'].isna())) & 
    (df['Adolescent_population_in_millions'] > 1)]

#clean the data
df = df.dropna(subset=['World_bank_income_group', 'WHO_region'])

# Visual Python: Visualization > Plotly

#Create scatterplot
fig = px.scatter(df, x='Year', y='Adolescent_population_in_millions', color='Country', 
                 title = "Adolescent population worldwide")

#title to the center
fig.update_layout(title_x=0.5)

#enlarge figure
fig.update_layout(width=1800, height=700)

fig.show()

from dash import Dash, dcc, html, Input, Output, callback
import plotly.express as px
import pandas as pd

#create app
app = Dash(__name__)
#create year-slider with 5-year steps
app.layout = html.Div([
    dcc.Graph(id='graph-with-slider'),
    dcc.Slider(
        df['Year'].min(),
        df['Year'].max(),
        step=5,
        value=df['Year'].min(),
        marks={str(year): str(year) for year in range(df['Year'].min(), df['Year'].max()+1, 5)},
        id='year-slider'
    )
])
@callback(
    Output('graph-with-slider', 'figure'),
    Input('year-slider', 'value'))

#create Geographic_Scatterplot
def update_figure(selected_year):
    filtered_df = df[df.Year == selected_year]

    fig = px.scatter_geo(filtered_df, locations="Country_ISO_3_code", color="WHO_region",
                     hover_name="Country", size="Adolescent_population_in_millions",
                     projection="natural earth",title="Geographic Scatterplot Adolescent Population worldwide, 5 years steps")

    fig.update_layout(transition_duration=100)

    fig.update_layout(title_x=0.5, width=1200, height=600)

    return fig

if __name__ == '__main__':
    app.run(debug=True, port=8051)

#Adolescent Population worldwide (grouped by WHO Region & World Bank Income Group) larger than 0.5 million

import plotly.express as px
import pandas as pd
from dash import Dash, dcc, html, Input, Output, callback

#load the data
df = pd.read_csv('./daten/WHO_adolescent_pop_1950_2050_age_10_19_both_sexes.csv')

# Define fixed colors for WHO_region and World_bank_income_group
unique_who_regions = df['WHO_region'].dropna().unique()
unique_income_groups = df['World_bank_income_group'].dropna().unique()

color_map = {
    **{region: f"rgba({i*50 % 255}, {100 + i*30 % 155}, {200 - i*20 % 100}, 1)" for i, region in enumerate(unique_who_regions)},
    **{group: f"rgba({150 + i*20 % 100}, {50 + i*40 % 155}, {250 - i*60 % 200}, 1)" for i, group in enumerate(unique_income_groups)},
}

# create app
app = Dash(__name__)

# year-slider with title of selected year
app.layout = html.Div([
    dcc.Graph(id='graph-with-slider'),
    dcc.Slider(
        df['Year'].min(),
        df['Year'].max(),
        step=None,
        value=df['Year'].min(),
        marks={str(Year): str(Year) for Year in df['Year'].unique()},
        id='year-slider'
    ),
    html.H2(id='year-title', children=f"Selected Year: {df['Year'].min()}")
])

@callback(
    Output('graph-with-slider', 'figure'),
    Output('year-title', 'children'),
    Input('year-slider', 'value'))

# build sunburst
def update_figure(selected_year):
    df_sb_clean = df.dropna(subset=['World_bank_income_group', 'WHO_region'])
    # filter for year and Adolescent_population_in_millions > 0.5
    filtered_df_1 = df_sb_clean[df_sb_clean.Year == selected_year]
    filtered_df = filtered_df_1[filtered_df_1.Adolescent_population_in_millions >= 0.5]

    countries = filtered_df.Country
    WBIC = filtered_df.World_bank_income_group
    adol_population = filtered_df.Adolescent_population_in_millions
    WHO_region = filtered_df.WHO_region

    # build filtered data frame
    filtered_df = pd.DataFrame({
        'Countries': countries,
        'World Bank Income Group': WBIC,
        'Adolescent Population': adol_population,
        'WHO Region': WHO_region
    })
    # order of sunburst
    fig = px.sunburst(filtered_df, path=['WHO Region', 'World Bank Income Group', 'Countries'], values='Adolescent Population', 
                  title="Adolescent Population worldwide (grouped by WHO Region & World Bank Income Group) > 0.5 million",         
                      color='WHO Region',  # Set the color dimension
                      color_discrete_map=color_map  # Apply the fixed color map
                     )
    fig.update_layout(title_x=0.5, width=1200, height=600)

    return fig, f"Selected Year: {selected_year}"

if __name__ == '__main__':
    app.run(debug=True, port=8052)

#Historic data: Adolescent population in WHO region Europe from 1950 to 2020, higher than 5 million
# Visual Python: Visualization > Plotly
fig = px.scatter(df, x='Year', y='Adolescent_population_in_millions', color='Country')

# Filter the DataFrame for High income
df_filtered = df[(df['WHO_region'] == 'Europe') 
    & (df['Adolescent_population_in_millions'] > 5)
    & (df.Year > 1950) & (df.Year < 2020)]

# Create the scatter plot with the filtered DataFrame
fig = px.scatter(df_filtered, x='Year', y='Adolescent_population_in_millions', color='Country', 
                 title="Historic data: Adolescent population in WHO region Europe from 1950 to 2020, higher than 5 million")

fig.update_layout(title_x=0.5)

fig.update_layout(width=1700, height=700)

fig.show()

#Adolescent population in WHO region Europe higher than 5 million
# Visual Python: Visualization > Plotly
fig = px.scatter(df, x='Year', y='Adolescent_population_in_millions', color='Country')

# Filter the DataFrame for High income
df_filtered = df[(df['WHO_region'] == 'Europe') 
    & (df['Adolescent_population_in_millions'] > 5)]

# Create the scatter plot with the filtered DataFrame
fig = px.scatter(df_filtered, x='Year', y='Adolescent_population_in_millions', color='Country', 
                 title="Adolescent population in WHO region Europe higher than 5 million")

fig.update_layout(title_x=0.5)

fig.update_layout(width=1700, height=700)

fig.show()

#Change from previous years for large European countries
# Visual Python: Visualization > Plotly

import pandas as pd
import plotly.express as px

large_European_countries = ['Russian Federation','Germany','Italy','Ukraine','United Kingdom of Great Britain and Northern Ireland',
                'Türkiye','France','Uzbekistan']

df_filtered_change = df[(df["Country"].isin(large_European_countries)) & (df["Year"] >= 1951) & (df["Year"] <= 2050)].copy()
df_filtered_change['change_from_previous_year_in_percent'] = pd.to_numeric(df_filtered_change
                                                                    ['change_from_previous_year_in_percent'], 
                                                                    errors='coerce')

fig = px.scatter(df_filtered_change, x='Year', y='change_from_previous_year_in_percent', color='Country', 
                 title="Change from previous years for large European countries")
fig.add_hline(y=0, line_dash="dash")

fig.update_layout(width=1200, height=800)

fig.show()

#Adolescent population in WHO region Africa higher than 5 million
# Visual Python: Visualization > Plotly
fig = px.scatter(df, x='Year', y='Adolescent_population_in_millions', color='Country')

# Filter the DataFrame for > 2 Million adolescents and WHO region == Europe
df_filtered = df[(df['Adolescent_population_in_millions'] > 5) & (df['WHO_region'] == 'Africa')]

# Create the scatter plot with the filtered DataFrame
fig = px.scatter(df_filtered, x='Year', y='Adolescent_population_in_millions', color='Country', 
                 title="Adolescent population in WHO region Africa higher than 5 million")

fig.update_layout(title_x=0.5)

fig.show()

#Change from previous years for large African countries
# Visual Python: Visualization > Plotly

import pandas as pd
import plotly.express as px

large_African_countries = ['Nigeria','Democratic Republic of the Congo','Ethiopia','United Republic of Tanzania',
                           'Uganda',]

df_filtered_change = df[(df["Country"].isin(large_African_countries)) & (df["Year"] >= 1951) & (df["Year"] <= 2050)].copy()
df_filtered_change['change_from_previous_year_in_percent'] = pd.to_numeric(df_filtered_change
                                                                    ['change_from_previous_year_in_percent'], 
                                                                    errors='coerce')

fig = px.scatter(df_filtered_change, x='Year', y='change_from_previous_year_in_percent', color='Country', 
                 title="Change from previous years for large African countries")
fig.add_hline(y=0, line_dash="dash")

fig.update_layout(width=1200, height=800)

fig.update_layout(title_x=0.5)

fig.show()

G7_countries = ['Canada','France','Germany','Italy','Japan',
                'United Kingdom of Great Britain and Northern Ireland','United States of America']

#Adolescent population in G7-countries
# Visual Python: Visualization > Plotly
fig = px.scatter(df, x='Year', y='Adolescent_population_in_millions', color='Country')

# Filter the DataFrame for High income
df_filtered = df[df['Country'].isin(G7_countries)]

# Create the scatter plot with the filtered DataFrame
fig = px.scatter(df_filtered, x='Year', y='Adolescent_population_in_millions', color='Country', 
                 title="Adolescent population in G7-countries")

fig.update_layout(title_x=0.5)

fig.update_layout(width=1700, height=700)

fig.show()

#Change from previous years for G7 countries
# Visual Python: Visualization > Plotly

import pandas as pd
import plotly.express as px

df_filtered_change = df[(df["Country"].isin(G7_countries)) & (df["Year"] >= 1951) & (df["Year"] <= 2050)].copy()
df_filtered_change['change_from_previous_year_in_percent'] = pd.to_numeric(df_filtered_change
                                                                    ['change_from_previous_year_in_percent'], 
                                                                    errors='coerce')

fig = px.scatter(df_filtered_change, x='Year', y='change_from_previous_year_in_percent', color='Country', 
                 title="Change from previous years for G7 countries")
fig.add_hline(y=0, line_dash="dash")

fig.update_layout(width=1500, height=800)

fig.show()

# Adolescent Population worldwide (grouped by WHO Region & World Bank Income Group) larger than 0.5 million

import plotly.express as px
import pandas as pd
from dash import Dash, dcc, html, Input, Output, callback

# Filter data for G7 countries
G7_countries = ['Canada', 'France', 'Germany', 'Italy', 'Japan', 
                'United Kingdom of Great Britain and Northern Ireland', 'United States of America']
df = pd.read_csv('./daten/WHO_adolescent_pop_1950_2050_age_10_19_both_sexes.csv')
df_filtered = df[df['Country'].isin(G7_countries)]

# Custom color map for G7 countries
country_colors = {
    'Canada': 'darkred',  # Dark Burgundy
    'France': 'darkblue',  # Dark Blue
    'Germany': 'darkgrey',  # Dark Grey
    'Italy': 'darkgreen',  # Dark Green
    'Japan': 'lightpink',  # Light Pink
    'United Kingdom of Great Britain and Northern Ireland': 'lightcoral',  # Light Red
    'United States of America': 'lightblue'  # Light Blue
}

# Create Dash app
app = Dash(__name__)

# App layout
app.layout = html.Div([
    dcc.Graph(id='graph-with-slider'),
    dcc.Slider(
        df_filtered['Year'].min(),
        df_filtered['Year'].max(),
        step=None,
        value=df_filtered['Year'].min(),
        marks={str(Year): str(Year) for Year in df_filtered['Year'].unique()},
        id='year-slider'
    ),
    html.H2(id='year-title', children=f"Selected Year: {df_filtered['Year'].min()}")
])

# Callback to update the graph and title
@callback(
    Output('graph-with-slider', 'figure'),
    Output('year-title', 'children'),
    Input('year-slider', 'value'))
def update_plot(selected_year):
    # Filter data for the selected year
    df_year = df_filtered[df_filtered['Year'] == selected_year]
    
    # Create the pie chart using Plotly with fixed colors
    fig = px.pie(
        df_year,
        names='Country',
        values='Adolescent_population_in_millions',
        title="Proportion of Adolescent Population in G7 Countries",
        color='Country',  # Specify the column for colors
        color_discrete_map=country_colors  # Use the custom color map
    )
    
    # Update layout for better visualization
    fig.update_layout(title_x=0.5, height=500, width=900)
    
    return fig, f"Selected Year: {selected_year}"

# Run the app
if __name__ == '__main__':
    app.run(debug=True, port=8053)

import dash
import dash_bootstrap_components as dbc
from dash import Input, Output, dcc, html

app = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP])

# Sidebar styles
SIDEBAR_STYLE = {
    "position": "fixed",
    "top": 0,
    "left": 0,
    "bottom": 0,
    "width": "16rem",
    "padding": "2rem 1rem",
    "background-color": "#f8f9fa",
}

# Content area styles
CONTENT_STYLE = {
    "margin-left": "18rem",
    "margin-right": "2rem",
    "padding": "2rem 1rem",
}

sidebar = html.Div(
    [
        html.H2("Sidebar", className="display-4"),
        html.Hr(),
        html.P("Adolescents worldwide 1950-2050", className="lead"),
        dbc.Nav(
            [
                dbc.NavLink("Geoplot worldwide: WHO Regions", href="/app1", active="exact"),
                dbc.NavLink("Sunburst: WHO Regions & World Bank Income Group", href="/app2", active="exact"),
                dbc.NavLink("Proportion of Adolescents in G7 countries", href="/app3", active="exact"),
            ],
            vertical=True,
            pills=True,
        ),
    ],
    style=SIDEBAR_STYLE,
)

content = html.Div(id="page-content", style=CONTENT_STYLE)

app.layout = html.Div([dcc.Location(id="url"), sidebar, content])

@app.callback(Output("page-content", "children"), [Input("url", "pathname")])
def render_page_content(pathname):

    if pathname == "/":
        pathname = "/app1"  # Redirect to App 1 by default
    
    if pathname == "/app1":
        return html.Iframe(src="http://localhost:8051", width="100%", height="1000")
    elif pathname == "/app2":
        return html.Iframe(src="http://localhost:8052", width="100%", height="1000")
    elif pathname == "/app3":
        return html.Iframe(src="http://localhost:8053", width="100%", height="1000")
    # If the user tries to reach a different page, return a 404 message
    return html.Div(
        [
            html.H1("404: Not found", className="text-danger"),
            html.Hr(),
            html.P(f"The pathname {pathname} was not recognised..."),
        ],
        className="p-3 bg-light rounded-3",
    )

if __name__ == "__main__":
    app.run_server(debug=False, port=8000)  # Run the main dashboard app on a different port

	Year	Country_ISO_3_code	Country	WHO_region	World_bank_income_group	Adolescent_population_in_millions	change_from_previous_year_in_percent
0	1950	AFG	Afghanistan	Eastern Mediterranean	Low income	1.7	#WERT!
1	1951	AFG	Afghanistan	Eastern Mediterranean	Low income	1.7	1.5
2	1952	AFG	Afghanistan	Eastern Mediterranean	Low income	1.7	1.5
3	1953	AFG	Afghanistan	Eastern Mediterranean	Low income	1.8	1.6
4	1954	AFG	Afghanistan	Eastern Mediterranean	Low income	1.8	1.7
...	...	...	...	...	...	...	...
23932	2046	ZWE	Zimbabwe	Africa	Lower middle income	5.0	1.3
23933	2047	ZWE	Zimbabwe	Africa	Lower middle income	5.1	1.2
23934	2048	ZWE	Zimbabwe	Africa	Lower middle income	5.1	1.1
23935	2049	ZWE	Zimbabwe	Africa	Lower middle income	5.2	1.0
23936	2050	ZWE	Zimbabwe	Africa	Lower middle income	5.2	0.9

No	Variable	Stats / Values	Freqs / (% of Valid)	Missing
1	Year [int64]	Mean (sd) : 2000.0 (29.2) min < med < max: 1950.0 < 2000.0 < 2050.0 IQR (CV) : 50.0 (68.6)	101 distinct values	0 (0.0%)
2	Country_ISO_3_code [object]	1. AFG 2. ALB 3. DZA 4. ASM 5. AND 6. AGO 7. AIA 8. ATG 9. ARG 10. ARM 11. other	101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 22,927 (95.8%)	0 (0.0%)
3	Country [object]	1. Afghanistan 2. Albania 3. Algeria 4. American Samoa 5. Andorra 6. Angola 7. Anguilla 8. Antigua and Barbuda 9. Argentina 10. Armenia 11. other	101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 101 (0.4%) 22,927 (95.8%)	0 (0.0%)
4	WHO_region [object]	1. Europe 2. Americas 3. Africa 4. Western Pacific 5. Eastern Mediterranean 6. South-East Asia 7. nan	6,161 (25.7%) 5,454 (22.8%) 5,151 (21.5%) 3,434 (14.3%) 2,222 (9.3%) 1,111 (4.6%) 404 (1.7%)	404 (1.7%)
5	World_bank_income_group [object]	1. High income 2. Upper middle income 3. Lower middle income 4. Low income 5. nan	8,585 (35.9%) 5,454 (22.8%) 5,151 (21.5%) 2,626 (11.0%) 2,121 (8.9%)	2,121 (8.9%)
6	Adolescent_population_in_millions [float64]	Mean (sd) : 4.5 (18.2) min < med < max: 0.0 < 0.6 < 259.9 IQR (CV) : 2.7 (0.2)	694 distinct values	0 (0.0%)
7	change_from_previous_year_in_percent [object]	1. 0.0 2. #DIV/0! 3. 2.2 4. 2.0 5. 2.1 6. 2.6 7. 1.9 8. 0.4 9. 2.9 10. 1.3 11. other	5,008 (20.9%) 455 (1.9%) 350 (1.5%) 326 (1.4%) 321 (1.3%) 313 (1.3%) 311 (1.3%) 310 (1.3%) 303 (1.3%) 302 (1.3%) 15,938 (66.6%)	0 (0.0%)

	Year	Country_ISO_3_code	Country	WHO_region	World_bank_income_group	Adolescent_population_in_millions	change_from_previous_year_in_percent
676	2020	AIA	Anguilla	Americas	NaN	0.0	0.0
2595	2020	BES	Bonaire, Sint Eustatius and Saba	Americas	NaN	0.0	0.0
4413	2020	HKG	China, Hong Kong SAR	NaN	High income	0.6	-0.7
4514	2020	MAC	China, Macao SAR	NaN	High income	0.1	-1.7
4918	2020	COK	Cook Islands	Western Pacific	NaN	0.0	0.0
7140	2020	FLK	Falkland Islands (Malvinas)	Americas	NaN	0.0	#DIV/0!
7645	2020	GUF	French Guiana	Americas	NaN	0.1	0.0
8756	2020	GLP	Guadeloupe	Americas	NaN	0.1	-1.8
9059	2020	GGY	Guernsey	Europe	NaN	0.0	0.0
9564	2020	VAT	Holy See	Europe	NaN	0.0	#DIV/0!
10978	2020	JEY	Jersey	Europe	NaN	0.0	0.0
11483	2020	XKX	Kosovo (in accordance with UN Security Council...	NaN	Upper middle income	0.3	-1.6
13402	2020	MTQ	Martinique	Americas	NaN	0.0	-2.3
13705	2020	MYT	Mayotte	Africa	NaN	0.1	3.1
14311	2020	MSR	Montserrat	Americas	NaN	0.0	0.0
15624	2020	NIU	Niue	Western Pacific	NaN	0.0	#DIV/0!
17543	2020	REU	Réunion	Africa	NaN	0.1	-0.7
17947	2020	BLM	Saint Barthélemy	Americas	NaN	0.0	0.0
18048	2020	SHN	Saint Helena	Africa	NaN	0.0	#DIV/0!
18452	2020	SPM	Saint Pierre and Miquelon	Americas	NaN	0.0	0.0
20977	2020	TWN	Taiwan, China	NaN	High income	2.2	-3.3
21482	2020	TKL	Tokelau	Western Pacific	NaN	0.0	#DIV/0!
23300	2020	VEN	Venezuela (Bolivarian Republic of)	Americas	NaN	5.4	-0.5
23502	2020	WLF	Wallis and Futuna	Western Pacific	NaN	0.0	0.0
23603	2020	ESH	Western Sahara	Africa	NaN	0.1	2.6

	Year	Country_ISO_3_code	Country	WHO_region	World_bank_income_group	Adolescent_population_in_millions	change_from_previous_year_in_percent
20977	2020	TWN	Taiwan, China	NaN	High income	2.2	-3.3
23300	2020	VEN	Venezuela (Bolivarian Republic of)	Americas	NaN	5.4	-0.5

Storytelling: Entwicklung der Anzahl von Jugendlichen weltweit, Europa, G7 aus deutscher Perspektive¶

Bibliotheken & Daten¶

Der Datensatz¶

Deskriptive Datenanalyse¶

Kategorienbetrachtung¶

Fehlende Werte suchen & löschen¶

EDA¶

Weltweite Entwicklung¶

Scatterplot der weltweiten jugendlichen Bevölkerung¶

*Geographischer Scatterplot (WHO-Regionen) der weltweiten jugendlichen Bevölkerung¶

*Sunburst (WHO-Region und Weltbank-Einkommensgruppe) der jugendlichen Bevölkerung weltweit, > 0,5 Mio Jugendliche¶

Europäische Entwicklung (& Vergleich mit Afrika)¶

Historische Daten: Jugendliche Bevölkerung in der WHO-Region Europa 1950-2020, > 5 Mio Jugendliche¶

Jugendliche Bevölkerung in der WHO-Region Europa 1950-2050, > 5 Mio Jugendliche¶

Prozentuelle Veränderung zum Vorjahr für die größten europäischen Länder¶

Vergleich: Entwicklung in der WHO-Region Afrika, >5 Mio. Jugendliche¶

Vergleich: Veränderung zum Vorjahr für die größten afrikanischen Länder¶

Entwicklung innerhalb der G7¶

Jugendliche Bevölkerung in G7-Ländern¶

Prozentuelle Veränderung zum Vorjahr für die G7-Länder¶

*Tortendiagramm der G7-Länder 1950-2050¶

INTERACTIVE DASHBOARD¶